positron-ai/Llama-3.2-1B-Instruct

TEXT GENERATIONPricing:Input $0.108 / Output $0.804Concurrent Unit Cost:1Model Size:1BQuant:BF16Context Size:32kTool Calling:SupportedPublished:Aug 25, 2026License:llama3.2Architecture:Transformer Featherless Exclusive Cold

The positron-ai/Llama-3.2-1B-Instruct is an unmodified redistribution of Meta's Llama-3.2-1B-Instruct, a 1 billion parameter instruction-tuned causal language model with a 32768 token context length. This model serves as a foundational snapshot for further quantizations published by Positron AI. It is designed for general instruction-following tasks, leveraging the Llama architecture for efficient performance in smaller-scale applications.

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Overview

positron-ai/Llama-3.2-1B-Instruct is a direct, unmodified redistribution of the meta-llama/Llama-3.2-1B-Instruct model. This 1 billion parameter instruction-tuned causal language model retains all original weights, tokenizer, and configuration files from Meta's upstream commit. Positron AI provides this snapshot primarily as a stable source for its own GPTQ quantizations.

Key Characteristics

  • Architecture: Based on the Llama 3.2 family, known for its robust performance across various language tasks.
  • Parameter Count: Features 1 billion parameters, making it suitable for applications requiring a smaller, more efficient model.
  • Context Length: Supports a substantial context window of 32768 tokens, allowing for processing longer inputs and maintaining conversational coherence.
  • Instruction-Tuned: Optimized for following instructions, making it versatile for a wide range of NLP applications.
  • Provenance: Byte-identical to the original Meta Llama-3.2-1B-Instruct, ensuring consistency and reliability.

Intended Use Cases

This model is well-suited for:

  • General Instruction Following: Performing tasks based on explicit instructions, such as summarization, question answering, and text generation.
  • Edge Device Deployment: Its smaller size makes it a candidate for deployment on devices with limited computational resources.
  • Research and Development: Serving as a base model for further fine-tuning or experimentation with the Llama 3.2 architecture.
  • Quantization Targets: Providing a stable, verified source for creating more efficient quantized versions of the model.

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